Prediction of building energy needs in early stage of design by using ANFIS

Ekici, Betül Bektaş; Aksoy, U. Teoman

doi:10.1016/j.eswa.2010.10.021

Cited by 161 publications

(40 citation statements)

References 30 publications

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“…Svalina et al [26] proposed an ANFIS based forecasting model for close price indices for a stock market for five days. Ekici and Aksoy [27] presented an ANFIS based building energy consumption forecasting model. More so, ANFIS is also applied to forecast electricity loads [28].…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

375

316

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In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

375

316

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show abstract

“…Heating, ventilation and air conditioning (HVAC) are the largest energy consumers in buildings [7]. Ekici and Aksoy [8] listed the parameters that affect building's energy requirements as follows: physical-environmental parameters (daily exterior temperature, solar radiation and wind speed and direction) and design parameters (shape factors, surface transparency, orientation, thermalphysical construction material properties and distances between buildings). The term bioclimatic (or sustainable) architecture refers to an alternative method of constructing buildings in which the local climate conditions are considered and diverse passive solar technologies are used with the aim of improving energy efficiency [9].…”

Section: Introductionmentioning

confidence: 99%

Review of bioclimatic architecture strategies for achieving thermal comfort

Manzano‐Agugliaro¹,

Manzano-Agugliaro²,

Sabio-Ortega³

et al. 2015

Renewable and Sustainable Energy Reviews

255

123

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“…This method integrates reasoning mechanism of fuzzy inference system (FIS) and learning capability of artificial neural network (ANN) simultaneously. As a structure; ANFIS consists of if-else rules and inputoutput data couples of fuzzy and it uses neural network's learning algorithms for training [12]. Such framework makes the ANFIS modeling more systematic and less reliant on expert knowledge [50,56].…”

Section: Anfis Methodsmentioning

confidence: 99%